A short-range data assimilation and numerical weather prediction system using the LETKF for ruban areas in Argentina

ARATA AMEMIYA; MARIA EUGENIA DILLON; PAULA SOLEDAD MALDONADO; GIMENA CASARETTO; FEDERICO CUTRARO; YANINA GARCIA SKABAR; JUAN RUIZ; MANUEL PULIDO; JORGE GACITUA GUTIERREZ; MAITE CANCELADA; DAICHI KITAHARA; MARTIN RUGNA; TAKEMASA MIYOSHI

Workshop; 6th Workshop on Mesoscale Modeling; 2023

The use of non-hydrostatic numerical weather prediction (NWP) models with a convectionpermitting grid spacing became a common approach to regional short-range precipitation forecasts(Gustaffson et al., 2018). The assimilation of frequent and high-resolution remote-sensingobservation data such as satellite and weather radar data plays an essential role in providing accurateinitial conditions for NWP model forecasts.The Argentinian National Weather Service (ANWS) has recently conducted studies utilizing highresolution NWP models. The operational deterministic and 20-member downscaled ensembleforecasts are produced using the Weather Research and Forecasting (WRF) model with a 4-kmhorizontal grid spacing (García Skabar et al., 2018). The experimental data assimilation system usingthe local ensemble transform Kalman filter (LETKF) with the WRF model over Argentinian domainhas been developed (Dillon et al., 2016; 2021). In this study, we developed a rapid-refresh ensembledata assimilation and numerical weather prediction system for short-range precipitation forecast forurban areas in Argentina, assimilating observation data from radar and surface weather stations. Thisstudy is conducted under PREVENIR, a 5-year cooperative project between Argentina and Japan forthe development of a total package of heavy rain and urban flood disaster prevention.